Search Results (11)

Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, with the avoiding local minima algorithm (PSO-ALM), uses a novel fitness function that can prevent the PSO search from trapping into the local minima and thus prevent the mobile robot from misidentifying the actual location. The fitness function penalizes nonsense solutions by introducing continuous integrity checks of solutions between two different consecutive locations. The proposed methodology enables accurate and real-time global localization of a mobile robot, given the underlying a priori map, with a consistent and predictable time complexity. Numerical simulations and real-world laboratory experiments with different a priori map accuracies have been conducted to prove the proper functioning of the method. The results have been compared with the benchmarks, i.e., the plain vanilla PSO and the built-in robot’s odometrical method, a genetic algorithm with included elitism and adaptive mutation rate (GA), the same GA algorithm with the included ALM algorithm (GA-ALM), the state-of-the-art plain vanilla golden eagle optimization (GEO) algorithm, and the same GEO algorithm with the added ALM algorithm (GEO-ALM). The results showed similar performance with the odometrical method right after recalibration and significantly better performance after some traveled distance. The GA and GEO algorithms with or without the ALM extension gave us similar results according to the accuracy of localization. The optimization algorithms’ performance with added ALM algorithms was much better at not getting caught in the local minimum, while the PSO-ALM algorithm gave us the overall best results. Full article

This study presents a novel approach to improving the efficiency and reliability of solar water pumping systems by integrating a proportional–integral–derivative (PID) controller with the Jellyfish Algorithm (PID-JC) and artificial neural networks (ANN). Solar water-pumping systems are gaining attention due to their sustainable and eco-friendly nature; however, their performance is often limited by fluctuating solar irradiance and varying water demand. To address these challenges, Monte Carlo simulations were employed to account for system uncertainties. Traditional PID controllers, although widely used, often struggle to adapt effectively to dynamic environmental conditions. The proposed system utilizes an ANN to predict solar irradiance and water demand patterns based on historical data, enabling real-time adjustments of pump operations through the PID-JC. This approach is inspired by the adaptive behavior of jellyfish in dynamic environments. The PID-JC adjusts PID parameters dynamically based on ANN predictions, optimizing pump performance. Simulation and experimental results conducted on a solar water-pumping system in Mrada City, Northeastern Libya, demonstrated significant improvements in water delivery, energy consumption, and system reliability compared to conventional PID controllers. The PID-JC’s ability to adapt to diverse environmental conditions ensures robust performance across various geographical locations and seasonal changes. Additionally, comparisons to other optimization algorithms, such as Firefly and Golden Eagle Optimization, show that the Jellyfish Algorithm outperforms them with a 6.30% improvement in the cost function and a 28.13% reduction in processing time compared to Firefly, and a 26.81% improvement in the cost function and a 20.69% reduction in processing time compared to Golden Eagle Optimization. Full article

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%. Full article

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time–frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time–frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods. Full article

A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm’s ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy. Full article

With the swift advancement of the geometric modeling industry and computer technology, traditional generalized Ball curves and surfaces are challenging to achieve the geometric modeling of various complex curves and surfaces. Constructing an interpolation curve for the given discrete data points and optimizing its shape have important research value in engineering applications. This article uses an improved golden eagle optimizer to design the shape-adjustable combined generalized cubic Ball interpolation curves with ideal shape. Firstly, the combined generalized cubic Ball interpolation curves are constructed, which have global and local shape parameters. Secondly, an improved golden eagle optimizer is presented by integrating Lévy flight, sine cosine algorithm, and differential evolution into the original golden eagle optimizer; the three mechanisms work together to increase the precision and convergence rate of the original golden eagle optimizer. Finally, in view of the criterion of minimizing curve energy, the shape optimization models of combined generalized cubic Ball interpolation curves that meet the C¹ and C² smooth continuity are instituted. The improved golden eagle optimizer is employed to deal with the shape optimization models, and the combined generalized cubic Ball interpolation curves with minimum energy are attained. The superiority and competitiveness of improved golden eagle optimizer in solving the optimization models are verified through three representative numerical experiments. Full article

Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by $42.41\%$ and improve dynamic tracking accuracy by $1.95\%$, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of $2.66\%$, $3.56\%$, and $4.24\%$ in dynamic tracking accuracy, respectively. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems. Full article

The determination of the rock elasticity modulus (EM) is an indispensable key step for the design of rock engineering problems. Traditional experimental analysis can accurately measure the rock EM, but it requires manpower and material resources, and it is time consuming. The EM estimation of new rocks using former published empirical formulas is also a possibility but can be attached of high uncertainties. In this paper, four types of metaheuristic optimization algorithms (MOA), named the backtracking search optimization algorithm (BSA), multi-verse optimizer (MVO), golden eagle optimizer (GEO) and poor and rich optimization algorithm (PRO), were utilized to optimize the random forest (RF) model for predicting the rock EM. A data-driven technology was used to generate an integrated database consisting of 120 rock samples from the literature. To verify the predictive performance of the proposed models, five common machine-learning models and one empirical formula were also developed to predict the rock EM. Four popular performance indices, including the root-mean-square error (RMSE), mean absolute error (MAE), the coefficient of determination (R²) and Willmott’s index (WI), were adopted to evaluate all models. The results showed that the PRO-RF model has obtained the most satisfactory prediction accuracy. The porosity (Pn) is the most important variable for predicting the rock EM based on the sensitive analysis. This paper compares the performance of the RF models optimized by using four MOA for the rock EM prediction. It provides a good example for the subsequent application of soft techniques on the EM and other important rock parameter estimations. Full article

Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches. Full article

The accurate estimation of reference crop evapotranspiration (ET_O) plays an important role in guiding regional water resource management and crop water content research. In order to improve the accuracy of ET_O prediction in regions with missing data, this study used the partial correlation analysis method to select factors that have a large impact on ET_O as input combinations to construct ET_O estimation models for typical stations in semi-arid regions of China. A biological heuristic optimization algorithm (Golden Eagle optimization algorithm (GEO) and Sparrow optimization algorithm (SSA)) and Extreme Learning Machine model (ELM) were combined to improve the estimation accuracy. The results showed that Ra was the primary factor affecting the ET_O model, with an importance range of 0.187–0.566. Compared with the independent ELM model, the hybrid model has higher accuracy and stability. The estimated value of the SSA-ELM model under five-factor input condition (Ra, RH, T_max, T_min, U₂) is closest to the standard value calculated by FAO56 PM: RMSE = 0.067–0.085, R² = 0.998–0.999, MAE = 0.050–0.066 and NSE = 0.998–0.999. In general, the combination of a partial correlation analysis algorithm and a hybrid model can be used to estimate ET_O with high accuracy under the condition of reducing input factors. Use of the first five factors extracted from the partial correlation analysis algorithm as input to build an ET_O estimation model based on SSA-ELM in China’s semi-arid regions is recommended, which can also provide a reference for ET_O estimation in similar regions. Full article